System and method for progressively transforming and coding digital data

ABSTRACT

A system and method facilitating progressively transforming and coding digital pictures is provided. The present invention via employment of a multi-resolution lapped transform provides for progressive rendering as well as mitigation of blocking artifacts and ringing artifacts as compared to many conventional compression systems. The invention includes a color space mapper, a multi-resolution lapped transform, a quantizer, a scanner and an entropy encoder. The multi-resolution lapped transform outputs transform coefficients, for example, first transform coefficients and second transform coefficients. A multi-resolution representation can be obtained utilizing second transform coefficients of the multi-resolution lapped transform. The color space mapper maps an input image to a color space representation of the input image. The color space representation of the input image is then provided to the multi-resolution lapped transform. The quantizer receives the first transform coefficients and/or the second transform coefficients and provides an output of quantized coefficients for use by the scanner and/or the entropy encoder. The scanner scans the quantized coefficients in order to produce a one-dimensional vector for use by the entropy encoder. The entropy encoder encodes the quantized coefficients received from the quantizer and/or the scanner resulting in data compression.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/206,506, filed Aug. 18, 2005, now U.S. Pat. No. 7,095,899 B2 andentitled, “SYSTEM AND METHOD FOR PROGRESSIVELY TRANSFORMING AND CODINGDIGITAL DATA”, which is a divisional of U.S. patent application Ser. No.10/109,291, filed Mar. 27, 2002, now U.S. Pat. No. 7,006,699 B2,entitled, “SYSTEM AND METHOD FOR PROGRESSIVELY TRANSFORMING AND CODINGDIGITAL DATA.” The entireties of the above-noted applications areincorporated herein by reference. This application is also related toU.S. patent application Ser. No. 11/206,507, filed on Aug. 18, 2005,entitled, “SYSTEM AND METHOD FOR PROGRESSIVELY TRANSFORMING AND CODINGDIGITAL DATA”, U.S. patent application Ser. No. 11/206,508, filed onAug. 18, 2005, entitled, “SYSTEM AND METHOD FOR PROGRESSIVELYTRANSFORMING AND CODING DIGITAL DATA”, and U.S. patent application Ser.No. 11/206,390, filed on Aug. 18, 2005, entitled, “SYSTEM AND METHOD FORPROGRESSIVELY TRANSFORMING AND CODING DIGITAL DATA”, and U.S. patentapplication Ser. No. 11/215,595, filed on Aug. 30, 2005, entitled,“SYSTEM AND METHOD FOR PROGRESSIVELY TRANSFORMING AND CODING DIGITALDATA.”

TECHNICAL FIELD

The present invention relates generally to digital picture processing,and more particularly to a system and method facilitating pictureencoding and/or decoding.

BACKGROUND OF THE INVENTION

The amount of information available via computers has dramaticallyincreased with the wide spread proliferation of computer networks, theInternet and digital storage means. With such increased amount ofinformation has come the need to transmit information quickly and tostore the information efficiently. Data compression is a technology thatfacilitates the effective transmitting and storing of information.

Data compression reduces an amount of space necessary to representinformation, and can be used for many information types. The demand forcompression of digital information, including images, text, audio andvideo has been ever increasing. Typically, data compression is used withstandard computer systems; however, other technologies make use of datacompression, such as but not limited to digital and satellite televisionas well as cellular/digital phones.

As the demand for handling, transmitting and processing large amounts ofinformation increases, the demand for compression of such data increasesas well. Although storage device capacity has increased significantly,the demand for information has outpaced capacity advancements. Forexample, an uncompressed digital picture can require 5 megabytes ofspace whereas the same picture can be compressed without loss andrequire only 2.5 megabytes of space. Thus, data compression facilitatestransferring larger amounts of information. Even with the increase oftransmission rates, such as broadband, DSL, cable modem Internet and thelike, transmission limits are easily reached with uncompressedinformation. For example, transmission of an uncompressed image over aDSL line can take ten minutes. However, the same image can betransmitted in about one minute when compressed thus providing aten-fold gain in data throughput.

In general, there are two types of compression, lossless and lossy.Lossless compression allows exact original data to be recovered aftercompression, while lossy compression allows for data recovered aftercompression to differ from the original data. A tradeoff exists betweenthe two compression modes in that lossy compression provides for abetter compression ratio than lossless compression because some degreeof data integrity compromise is tolerated. Lossless compression may beused, for example, when compressing critical text, because failure toreconstruct exactly the data can dramatically affect quality andreadability of the text. Lossy compression can be used with pictures ornon-critical text where a certain amount of distortion or noise iseither acceptable or imperceptible to human senses.

Picture compression is a particularly important technical problem,because digital pictures are a significant portion of the informationgrowth referred to previously. Most Web pages today contain manypictures, and many office documents also contain several pictures. Theuse of digital cameras keeps growing at a fast pace; many users haveliterally thousands of pictures taken from such cameras.

One of the most popular and widely used techniques of picturecompression is the Joint Photographic Experts Group (JPEG) standard. TheJPEG standard operates by mapping an 8×8 square block of pixels into thefrequency domain by using a discrete cosine transform (DCT).Coefficients obtained by the DCT are divided by a scale factor androunded to the nearest integer (a process known as quantizing) and thenmapped to a one-dimensional vector via a fixed zigzag scan pattern. Thisone-dimensional vector is encoded using a combination of run-lengthencoding and Huffman encoding.

Although JPEG is a popular and widely used compression technique, it hasseveral disadvantages. For example, one disadvantage of JPEG is that atlow bit rates the DCT produces irregularities and discontinuities in areconstructed image (known as tiling or blocking artifacts). Blockingartifacts cause the boundary between groups of 8×8 blocks of pixels tobecome visible in the reconstructed image. These blocking artifactscause an undesirable degradation in image quality. Another disadvantageof JPEG is that JPEG cannot perform image reconstruction that isprogressive in fidelity. In other words, if an image is encoded at acertain fidelity and a lower fidelity is later desired (for example, dueto limited bandwidth or storage availability), the image must be decodedand re-encoded.

Some of the disadvantages of JPEG are mitigated by the new JPEG2000,which replaces the DCT by wavelet transforms. Although wavelets providesmooth signal reconstruction without blocking artifacts, they can leadto an increase in blurring and ringing artifacts. Furthermore, JPEG2000uses a relatively complex coefficient encoding system, resulting in acompression technique that can be 3× (or more) slower than JPEG.

SUMMARY OF THE INVENTION

The following presents a simplified summary of the invention in order toprovide a basic understanding of some aspects of the invention. Thissummary is not an extensive overview of the invention. It is notintended to identify key/critical elements of the invention or todelineate the scope of the invention. Its sole purpose is to presentsome concepts of the invention in a simplified form as a prelude to themore detailed description that is presented later.

The present invention provides for a digital picture compression systemand methodology that employs a multi-resolution lapped transform thatreceives input values (e.g., from a color space mapper), and providesfor progressive rendering. The multi-resolution lapped transformutilizes hierarchical lapped bi-orthogonal transforms that mitigate“blocking artifacts” associated with many conventional picturecompression systems employing discrete cosine transform (DCT), such asJPEG. Further, the use of bi-orthogonal lapped transforms reducesnoticeable “ringing artifacts” compared with conventional DCT-basedpicture compression systems.

One particular aspect of the invention provides for a picturecompression system having the color space mapper, the multi-resolutionlapped transform, a quantizer, a scanner and/or an entropy encoder. Themulti-resolution lapped transform outputs transform coefficients, forexample, first transform coefficients and second transform coefficients.A multi-resolution representation can be obtained utilizing secondtransform coefficients of the multi-resolution lapped transform. Thecolor space mapper maps an input image to a color space representationof the input image (e.g., YUV and/or YC_(O)C_(G)). The color spacerepresentation of the input image is then provided to themulti-resolution lapped transform. The quantizer receives the firsttransform coefficients and/or the second transform coefficients andprovides an output of quantized coefficients for use by the scannerand/or the entropy encoder. The scanner scans the quantized coefficientsin order to produce a one-dimensional vector for use by the entropyencoder. A Peano-like scanning order can be utilized by the scanner. Theentropy encoder encodes the quantized coefficients received from thequantizer and/or the scanner resulting in data compression. The entropyencoder can utilize an adaptive run-length encoder.

Another aspect of the present invention provides for a picturecompression system having a color space mapper, a lossless transform andan entropy encoder. The lossless transform receives input values fromthe color space mapper and utilizes a lossless transform (e.g., ahierarchical Hadamard transform).

Yet another aspect of the present invention provides for a picturedecompression system having an entropy decoder, an inverse transform anda reverse color space mapper. The entropy decoder receives a bit stream(e.g., produced by a corresponding entropy encoder) and decodes the bitstream. The entropy decoder can utilize an adaptive run-length decoder.

The inverse transform receives input values from the entropy decoder andutilizes inverse transforms (e.g., inverse hierarchical lappedbi-orthogonal or inverse hierarchical Hadamard). The inverse transformprovides output values to the reverse color space mapper. The reversecolor space mapper maps input values (e.g., YUV and/or YC_(O)C_(G)) toan RGB output image.

Another aspect of the present invention provides for the picturecompression system to be employed in a vast array of document imageapplications, including, but not limited to, segmented layered imagesystems, photocopiers, document scanners, optical character recognitionsystems, personal digital assistants, fax machines, digital cameras,digital video cameras and/or video games.

Other aspects of the present invention provide methods for datacompression/encoding, data decompression/decoding, scanning a chunk ofcoefficients, color mapping and reverse color mapping. Further providedare a computer readable medium having computer usable instructions for asystem for picture compression and a computer readable medium havingcomputer usable instructions for a system for picture decompression.Also provided is a data packet adapted to be transmitted between two ormore computer processes comprising information associated thatfacilitates data compression, the information comprising first transformcoefficients based, at least in part, upon a lapped bi-orthogonaltransform of input values, and second transform coefficients based, atleast in part, upon a lapped bi-orthogonal transform of at least onefirst transform coefficient. A data packet adapted to be transmittedbetween two or more computer components that facilitates datacompression comprising a data field comprising first transformcoefficients based, at least in part, upon a hierarchical Hadamardtransform of input values, and, second transform coefficients based, atleast in part, upon a hierarchical Hadamard transform of at least onefirst transform coefficient is further provided.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the invention are described herein in connectionwith the following description and the annexed drawings. These aspectsare indicative, however, of but a few of the various ways in which theprinciples of the invention may be employed and the present invention isintended to include all such aspects and their equivalents. Otheradvantages and novel features of the invention may become apparent fromthe following detailed description of the invention when considered inconjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a picture compression system in accordancewith an aspect of the present invention.

FIG. 2 is a block diagram of a bi-orthogonal lapped transform inaccordance with an aspect of the present invention.

FIG. 3 is a block diagram of a multi-resolution lapped transform inaccordance with an aspect of the present invention.

FIG. 4 is a block diagram of a multi-resolution lapped transform inaccordance with an aspect of the present invention.

FIG. 5 is a block diagram of a multi-resolution lapped transform inaccordance with an aspect of the present invention.

FIG. 6 is a block diagram illustrating a four by four data block inaccordance with an aspect of the present invention.

FIG. 7 is a block diagram illustrating a Peano-like scanning pattern fora sixteen by sixteen data macroblock in accordance with an aspect of thepresent invention.

FIG. 8 is a block diagram illustrating a scanning pattern for a four byfour block of second level coefficients in accordance with an aspect ofthe present invention.

FIG. 9 is a block diagram of a picture compression system in accordancewith an aspect of the present invention.

FIG. 10 is a block diagram of a length-4 Hadamard transform inaccordance with an aspect of the present invention.

FIG. 11 is a block diagram of a picture decompression system inaccordance with an aspect of the present invention.

FIG. 12 is a flow chart illustrating a methodology for datacompression/encoding in accordance with an aspect of the presentinvention.

FIG. 13 is a flow chart illustrating a methodology for datadecompression/decoding in accordance with an aspect of the presentinvention.

FIG. 14 is a flow chart illustrating a methodology for scanning a chunkof coefficients in accordance with an aspect of the present invention.

FIG. 15 is a block diagram illustrating a lossless color space forwardmapper component and a reverse mapper component in accordance with anaspect of the present invention.

FIG. 16 is a flow chart illustrating a methodology for color spacemapping in accordance with an aspect of the present invention.

FIG. 17 is a flow chart illustrating a methodology for reverse colorspace mapping in accordance with an aspect of the present invention.

FIG. 18 illustrates an example operating environment in which thepresent invention may function.

FIG. 19 is a schematic block diagram of an exemplary communicationenvironment in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present invention.

As used in this application, the term “computer component” is intendedto refer to a computer-related entity, either hardware, a combination ofhardware and software, software, or software in execution. For example,a computer component may be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, a program, and/or a computer. By way of illustration, bothan application running on a server and the server can be a computercomponent. One or more computer components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers.

Referring to FIG. 1, a picture compression system 100 in accordance withan aspect of the present invention is illustrated. As noted above, thesystem 100 of the present invention via employment of a multi-resolutionlapped transform 120 provides for progressive rendering as well asmitigation of blocking artifacts and ringing artifacts as compared tomany convention compression systems. The picture compression system 100includes a color space mapper 110, a multi-resolution lapped transform120, a quantizer 130, a scanner 140 and an entropy encoder 150.

The color space mapper 110 maps an input image to a color spacerepresentation of the input image. The color space representation of theinput image is then provided to the multi-resolution lapped transform120. In one example, the color space mapper 110 maps the input image toa YUV representation of an RGB input image (e.g., represented by red,green and blue components). A YUV representation uses a luminancecomponent denoted by Y and chrominance-red denoted by U andchrominance-blue denoted by V.

In another example, the color space mapper 110 maps the input image to aYC_(o)C_(g) representation. The YC_(o)C^(g) representation utilizesluminance represented by Y, chrominance-orange represented by C_(o) andchrominance-green represented by C_(g). The RGB input components aremapped to YC_(o)C_(g) (e.g., as an alternative to the conventional YUVdescribed above) utilizing the transform:

$\begin{matrix}{\begin{bmatrix}Y \\C_{o} \\C_{g}\end{bmatrix} = {\left. {\begin{bmatrix}1 & 2 & 1 \\2 & 0 & {- 2} \\{- 1} & 2 & {- 1}\end{bmatrix}\begin{bmatrix}R \\G \\B\end{bmatrix}}\Leftrightarrow\begin{bmatrix}R \\G \\B\end{bmatrix} \right. = {\begin{bmatrix}1 & 1 & {- 1} \\1 & 0 & 1 \\1 & {- 1} & {- 1}\end{bmatrix}\begin{bmatrix}Y \\C_{o} \\C_{g}\end{bmatrix}}}} & (1)\end{matrix}$

Significantly, an advantage of YC_(o)C_(g) color space mapping is thatmapping from RGB to YC_(o)C_(g) and the inverse conversion fromYC_(o)C_(g) to RGB can be accomplished utilizing integer arithmetic,thus reducing computational overhead. Further, the inverse conversioncan be performed without multiplication. The YC_(o)C_(g) color spacerepresentation can result in significantly better compressionperformance than the popular YUV because it is a better approximation tostatistically optimal spaces that are obtained from a principalcomponent analysis on modern digital picture data.

It is to be appreciated that numerous other color space representationsare contemplated conducive to facilitating data compression utilizing amulti-resolution lapped transform in connection with the subjectinvention. Any suitable color space representation for employment inconnection with the present invention is intended to fall within thescope of the appended claims. Further, any suitable computer process(es)can be performed by the color space mapper 110 (e.g., integer and/orfloating point) in accordance with the present invention.

The multi-resolution lapped transform 120 receives input values, forexample, from the color space mapper 110. The multi-resolution lappedtransform 120 can allow the picture compression system 100 to haveprogressive rendering. The multi-resolution lapped transform 120utilizes hierarchical lapped bi-orthogonal transforms. By using lappedtransforms, “blocking artifacts” of conventional picture compressionsystems employing discrete cosine transform (DCT), such as JPEG, can bereduced. Further, the use of lapped bi-orthogonal transforms reducesnoticeable “ringing artifacts” compared with conventional DCT-basedpicture compression systems.

Referring briefly to FIG. 2, a lapped bi-orthogonal transform (LBT) 200in accordance with an aspect of the present invention is illustrated.The LBT 200 includes a first DCT-like transform 210 (e.g., similar to aDCT, but not identical to it) having four inputs x(0), x(1), x(2) andx(3), associated with a first block of data. The LBT 200 also includessecond DCT-like transform 220 having four inputs x(0), x(1), (2) andx(3) associated with a second block of data. The LBT 200 has fouroutputs 230, X(0), X(1), X(2) and X(3). As illustrated in FIG. 2, in thedirect transform (e.g., data compression/encoding), data is processedfrom left to right, and in the inverse transform (e.g., datadecompression/decoding) data is processed from right to left. Thescaling factors can be different for the direct (D) and inverse (I)transforms.

In order to perform the lapping portion of the transform, the output 230for a block of data input to the second DCT-like transform 220 isdependent upon the inputs of a previous block of data input to the firstDCT-like transform 210. In the instance where no previous block of datahas been input (e.g., upon initialization and/or at corner(s) of apicture), the input values to the first DCT-like transform 210 would notbe completely defined. Specifically x(0) and x(1) fall outside thepicture boundary if the first DCT-like transform 210 is the first onefor a row or column. In that case, an exemplary solution is to useeven-symmetric extension, by setting x(1)=x(2) and x(0)=x(3). A similarsymmetric reflection is applied to the last DCT-like transform 210 for apicture row or column. In both cases, it is easy to see that the firstand last DCT-like transform 210 for a row or column can be replaced bysimple 2×2 operators (e.g., two distinct inputs, two distinct outputs).

In one example, substantially all computations in the LBT 200 can beaccomplished using only integer arithmetic and no multiplications. Forexample, for a given value z, a new value z/2 is implemented as a rightshift: z>>1. Further, the quantity 1.25z can be implemented by addingright shifting z twice and adding that amount to z (e.g., z+(z>>2)).While this implementation can result in small truncation errors producedby the shifts (as long as the data is appropriately scaled), notably theimplementation is generally processor independent, since the result willtypically be the same regardless of the processor used to perform thetransform. Accordingly, substantially all implementations of the systemsand methods of the present invention can lead to substantially similarcompressed files for the same original picture bitmap, unlikeconventional data compression systems such as JPEG, MPEG, and otherstandards.

Turning briefly to FIG. 3, a multi-resolution lapped transform 300 inaccordance with an aspect of the present invention is illustrated. Themulti-resolution lapped transform 300 includes a first initial LBT 310 ₁through an Sth initial LBT 310 _(S), S being an integer greater than orequal to one. The first initial LBT 310 ₁ through the Sth initial LBT310 _(S) can be referred to collectively as the initial LBT 310. Themulti-resolution lapped transform 300 also includes a secondary LBT 320.The multi-resolution lapped transform 300 can be utilized, for example,by the multi-resolution lapped transform 120.

The initial LBT 310 receives input values (e.g., from the color spacemapper 110). The initial LBT 310 processes the input values and outputsfirst transform coefficients based, at least in part, upon a lappedbi-orthogonal transform of the input values. For example, the initialLBT 310 can utilize the exemplary LBT 200 set forth previously.

First transform coefficient(s) of the first initial LBT 3101 through theSth initial LBT 310 _(S) are provided as inputs to the secondary LBT320. In one example, the low frequency coefficient (e.g., DC) isprovided by the initial LBT 310 to the secondary LBT 320. The secondaryLBT 320 processes the first transform coefficient(s) and outputs secondtransform coefficient(s) based, at least in part, upon a lappedbi-orthogonal transform of the input first transform coefficient(s). Forexample, the secondary LBT 320 can utilize the exemplary LBT 200 setforth previously.

A multi-resolution representation can be obtained utilizing secondtransform coefficients of the secondary lapped bi-orthogonal transform320. For example, a bit map reconstructed by applying only the secondlevel of an inverse hierarchical LBT would recover a picture bitmap thatrepresents a 4×-downsampled version of the original comparable to animage resulting from conventional bicubic downsampling filter(s).

Referring briefly to FIG. 4, a multi-resolution lapped transform 400 inaccordance with an aspect of the present invention is illustrated. Thetransform 400 includes a first initial LBT 410 ₁, a second initial LBT410 ₂, a third initial LBT 410 ₃, a fourth initial LBT 410 ₄ and asecondary LBT 420. The low frequency coefficient output of the firstinitial LBT 410 ₁, the second initial LBT 410 ₂, the third initial LBT410 ₃ and the fourth initial LBT 410 ₄ are provided as inputs to thesecondary LBT 420. The multi-resolution lapped transform 400 can beutilized, for example, by the multi-resolution lapped transform 120.

Next, turning to FIG. 5, a multi-resolution lapped transform 500 inaccordance with an aspect of the present invention is illustrated. Thetransform 500 includes an initial LBT 510 and a secondary LBT 520. Thelow frequency coefficient outputs of the initial LBT 510 aresequentially provided to the secondary LBT 520. The secondary LBT 520provides a second level coefficient output once sufficient low frequencycoefficients have been received from the initial LBT 510. Themulti-resolution lapped transform 500 can be utilized, for example, bythe multi-resolution lapped transform 120.

For processing images, a two-dimensional transform is utilized. Toachieve the two-dimensional transform, the LBTs discussed previously canbe applied to the rows and columns of the input values (e.g., of each ofthe Y, C_(o), and C_(g) received from the color space mapper 110). Inone example, in order to reduce computational overhead, entire columnsare not processed, since each column access spans almost the entirebitmap array which would require off-cache memory access. Instead, inaccordance with the present invention, an internally “rolling buffer”approach, in which part of the column transform is performed after eachset of four rows is processed can be utilized. In this manner, thetwo-dimensional transform can be computed in only one scan of theoriginal bitmap.

Referring back to FIG. 1, the quantizer 130 receives the first transformcoefficients and/or the second transform coefficients and provides anoutput of quantized coefficients for use by the scanner 140 and/or theentropy encoder 150. The quantizer 130 typically introduces a loss ofinformation into the picture compression system 100. The loss resultsfrom the quantization of the coefficient, since for a transformed valueY, its quantized version is typically given by r=int[(Y+f)/s], where sis a step size of the quantizer 130, with |f| typically equal to s/2 andsign(f)=sign(Y). Thus, as the step size s increases, the correspondingdynamic range of r is reduced as is the likelihood of r equaling zero.During decompression (e.g., decoding), an approximation to Y isrecovered typically by Ŷ=r×s. Accordingly, the smaller the step size sthe closer the approximation Ŷ≈Y. As the step size increases, typicallydata compression is more effective; however, greater loss is introduced.In one example, in order to reduce computational overhead, the quantizer130 utilizes integer arithmetic, e.g., by scaling the values by aninteger factor Z, and approximating Z/s by an integer.

The scanner 140 scans the quantized coefficients in order to produce aone-dimensional vector for use by the entropy encoder 150. In oneexample, the scanner 140 utilizes row-wise scanning, while in anotherexample, the scanner utilizes column-wise scanning. In yet anotherexample, the scanner 140 utilizes a zigzag pattern, such as inconventional JPEG data compression systems.

In a fourth example, the quantized coefficients are scanned in adifferent but still fixed (data-independent) pattern (e.g., to avoidrandom data access). Referring briefly to FIG. 6, a four by four blockof coefficients is illustrated in accordance with an aspect of thepresent invention. Next, turning to FIG. 7, a Peano-like scanningpattern for a sixteen by sixteen data macroblock (a group of L blocks,in this case L=4) in accordance with an aspect of the present inventionis illustrated. FIG. 8 illustrates a scanning pattern for a four by fourblock of second level coefficients (such as those generated by thesecondary lapped transform of 320, 420, or 520) in accordance with anaspect of the present invention.

For each macroblock (e.g., generated by a hierarchical cascade of 4×4transforms), the transform value is read into one of six groups ofcoefficients. Consecutive values of each group are read from Mconsecutive macroblocks (a “chunk”), and the six groups are concatenatedas one 256M-long vector that is sent to the entropy coder. Thus, eachchunk can be encoded independently. Independent encoding allows for eachchunk to be independently decoded, thus allowing for only a portion ofthe picture bitmap to be decoded, if so desired.

The scanning pattern set forth in FIGS. 7 and 8 is a combination of aspatial-frequency-ordered scan for the DC coefficients (e.g., that wentthrough two levels of LBT) and a Peano plus spatial-frequency-orderedscan for the AC coefficients (e.g., which went through only the firstlevel of LBT). The Peano component (the shaded arrow pattern in FIG. 7)is used so that for each group of AC coefficients that are adjacent in aparticular group come from adjacent 4×4 blocks.

Thus, Group 0 comprises a particular second level DC coefficient thatpassed through the second level LBT of each macroblock. Group 1 throughGroup 5 scanning can then be performed for each macroblock with Group 1through Group 5 scanning then being performed for the next macroblockand so on. Group 1 comprises, for a macroblock, the remaining DCcoefficients that went through the second level LBT for the macroblock.Group 2 comprises, for each LBT block of the macroblock, the illustratedcoefficient values. Group 3 comprises, for each LBT block of themacroblock, the illustrated coefficient values. Group 4 comprises, foreach LBT block of the macroblock, the illustrated coefficient values.Group 5 comprises, for each LBT block of the macroblock, the illustratedcoefficient values.

Referring back to FIG. 1, the entropy encoder 150 encodes the quantizedcoefficients received from the quantizer 130 and/or the scanner 140. Thecolor space mapper 110, multi-resolution lapped transform 120, thequantizer 130 and/or the scanner 140 have converted original pixel datainto a vector of integer numbers with a reduced dynamic range and longstrings of zeros—though no data compression. The entropy encoder 150encodes these quantized coefficients, thus resulting in datacompression.

In one example, an adaptive run-length coder is utilized by the encoder150. Each bit plane of the input vector is processed in order, startingat the most significant bit (MSB), and ending at the least significantbit. For each coefficient, a bit is labeled as “significant”, if nononzero bit has been encoded yet, or “refinement”, if a significant bitfor that coefficient has already been encoded. Refinement bits areequally likely to be zero or one, so they are copied unmodified to thebit stream. Significant bits are more likely to be zero, and so they areencoded via an adaptive and efficient run-length encoder, which producessymbols according to the rule described in Table 1.

TABLE 1 Run-length encoding rule for significant bits, with parameter k.Codeword Input bit sequence 0 Complete run of 2^(k) zeros 1 c 0 Partialrun of c < 2^(k) zeros followed by a 1, sign of coefficient = ‘+’ (c isa k-bit number) 1 c 1 Partial run of c < 2^(k) zeros followed by a 1,sign of coefficient = ‘−’

The parameter k controls the compression efficiency of the run-lengthencoder. The larger the value of k, the longer the string of zero bitsthat can be represented by a codeword consisting of a single bit=0, andthus the higher the compression. The parameter k can be “tuned” to thestatistics of the data such that that 2^(k) is approximately equal tothe most likely length of strings of zeros.

In traditional run-length coding, the parameter k is either fixed orregularly updated and added to the bit stream (because the decoder needsto know of any changes in k). Both approaches can lead to a significantperformance penalty, though, for two reasons. First, the input data hasusually varying statistics, so k needs to be varied in order to tracksuch changes. Second, updating the value of k by copying it into the bitstream adds significant overhead, because several bits are needed torepresent the value of k.

Thus, in the adaptive run-length coder of this example, a backwardadaptation rule for k is used. By backward it is meant that k isadjusted based on the encoded symbols, not on the input data. Thus, aslong as encoder and decoder use the same adaptation rules, the values ofk need not be transmitted. The basic adaptation rule is quite simple. Ifthe codeword is zero, that means a run of zeros has just been observed,it is anticipated that runs of zeros are more likely, and thus k isincreased. If the codeword starts with a 1, that means an incomplete runhas just been observed, so it is anticipated that runs of zeros are lesslikely, and thus k is reduced.

Integer increases in k can lead to too fast of adaptation with aresultant penalty in compression performance. Accordingly, k can beadjusted by fractional amounts (e.g., by increasing and decreasing ascaled version of k).

The run-length encoding symbols can be terminated at the end of each bitplane and a field with the length of the encoded data for each bit planeadded. Accordingly, the bit stream can be parsed and the leastsignificant bit plate can be removed, if desired. This is equivalent tore-encoding the data with half the step size. Thus, recompressing thatdata be accomplished by simply parsing out some bits from the compressedfile. As such, fidelity scalability can be achieved.

It is to be appreciated that numerous other entropy encoding techniques(e.g., adaptive arithmetic encoding) are contemplated, conducive tofacilitating data compression utilizing a multi-resolution lappedtransform in connection with the subject invention. Any suitable entropyencoding technique for employment in connection with the presentinvention is intended to fall within the scope of the appended claims.

While FIG. 1 is a block diagram illustrating components for the picturecompression system 100, it is to be appreciated that the color spacemapper 110, the multi-resolution lapped transform 120, the quantizer130, the scanner 140 and/or the entropy encoder 150 can be implementedas one or more computer components, as that term is defined herein.Thus, it is to be appreciated that computer executable componentsoperable to implement the picture compression system 100, the colorspace mapper 110, the multi-resolution lapped transform 120, thequantizer 130, the scanner 140 and/or the entropy encoder 150 can bestored on computer readable media including, but not limited to, an ASIC(application specific integrated circuit), CD (compact disc), DVD(digital video disk), ROM (read only memory), floppy disk, hard disk,EEPROM (electrically erasable programmable read only memory) and memorystick in accordance with the present invention.

Turning next to FIG. 9, a lossless picture compression system 900 inaccordance with an aspect of the present invention is illustrated. Thepicture compression system 900 includes a color space mapper 110, alossless transform 910 and an entropy encoder 150.

The lossless transform 910 receives input values, for example, from thecolor space mapper 110. The lossless transform 910 utilizes a losslesstransform. For lossless encoding, there is no need to use an overlappingtransform, since there won't be blocking artifacts (because there is noquantization involved). For example, a hierarchical Hadamard transformcan be utilized by the lossless transform 910. Referring briefly to FIG.10, the hierarchical transform structure 1010 can be utilized, but withthe 4×4 transform modules implemented by the lossless Hadamard structure1020. It is to be appreciated that the lossless transform 1010 can beimplemented as one or more computer components, as that term is definedherein.

Turning to FIG. 11, a picture decompression system 1100 in accordancewith an aspect of the present invention is illustrated. The system 1100includes an entropy decoder 1110, a reverse scanner 1120, an inversequantizer 1130, an inverse transform component 1140 and a reverse colorspace mapper 1150.

The entropy decoder 1110 receives a bit stream (e.g., produced by acorresponding entropy encoder) and decodes the bit stream. In oneexample, the entropy decoder 1110 utilizes an adaptive run-lengthdecoder similar in operation to that described above with regard to theencoder 150.

The reverse scanner 1120 reverse scans the entropy decoded input bitstream received from the entropy decoder 1110. The reverse scanner 1120provides an output of quantized first transform coefficients and/orquantized second transform coefficients to the inverse quantizer 1130.

In one example, the reverse scanner 1120 utilizes row-wise reversescanning, while in another example, the reverse scanner utilizes reversecolumn-wise scanning. In yet another example, the reverse scanner 1120utilizes a zigzag pattern, such as in conventional JPEG data compressionsystems. In a fourth example, the entropy decoded input bit stream isscanned in a different but still fixed (data-independent) pattern (e.g.,to avoid random data access), such as a reverse Peano-like scanningpattern.

The inverse quantizer 1130 inverse quantizes the quantized firsttransform coefficients and/or quantized second transform coefficientsreceived from the reverse scanner 1120. The inverse quantizer 1130provides an output of unquantized coefficients (e.g., first transformcoefficients and/or second transform coefficients).

The inverse transform component 1140 receives output values from theinverse quantizer 1130. In one example, the inverse transform component1140 utilizes inverse hierarchical lapped bi-orthogonal transforms andprovides output values to the reverse color space mapper 1150. Forexample, the inverse transform component 1140 can employ the inverse ofthe multi-resolution lapped transform 200 of FIG. 2 (e.g., from right toleft). In another example, the inverse transform component 1140 utilizesan inverse lossless transform (e.g., an inverse hierarchical Hadamardtransform), for example, to decode a picture bitmap that was originallyencoded with the lossless encoding system 900. For example, the inversetransform (e.g., lossless) can essentially revert the computations inthe lossless module 1020 (e.g., in reverse order).

The reverse color space mapper 1150 maps input values to an RGB outputimage. In one example, the reverse color space mapper 1150 maps a YUVrepresentation to an RGB output. In another example, the reverse colorspace mapper 1150 maps a YC_(o)C_(g) representation to an RGB output. Itis to be appreciated that numerous other color space representations arecontemplated conducive to facilitating data decompression utilizing, forexample, an inverse hierarchical bi-orthogonal lapped transform inconnection with the subject invention. Any suitable color spacerepresentation for employment in connection with the present inventionis intended to fall within the scope of the appended claims. Further,any suitable computer process(es) can be performed by the reverse colorspace mapper 1150 (e.g., integer and/or floating point) in accordancewith the present invention.

It is to be appreciated that the entropy decoder 1110, the reversescanner 1120, the inverse quantizer 1130, the inverse transform 1140and/or reverse color space mapper 1150 can be computer components.

In view of the exemplary systems shown and described above,methodologies that may be implemented in accordance with the presentinvention will be better appreciated with reference to the flow chartsof FIGS. 12, 13, 14, 16 and 17. While, for purposes of simplicity ofexplanation, the methodologies are shown and described as a series ofblocks, it is to be understood and appreciated that the presentinvention is not limited by the order of the blocks, as some blocks may,in accordance with the present invention, occur in different ordersand/or concurrently with other blocks from that shown and describedherein. Moreover, not all illustrated blocks may be required toimplement the methodologies in accordance with the present invention.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more components. Generally, program modules include routines,programs, objects, data structures, etc. that perform particular tasksor implement particular abstract data types. Typically the functionalityof the program modules may be combined or distributed as desired invarious embodiments.

Turning to FIG. 12, a methodology 1200 for data compression/encoding inaccordance with an aspect of the present invention is illustrated. At1210, for each macroblock, a transform for each block is performed. Inone example, a bi-orthogonal lapped transform is employed (e.g., lossymode). In another example, a lossless Hadamard transform (e.g., losslessHadamard structure 1020) is utilized (e.g., lossless mode). At 1220, atransform is performed on low frequency coefficient(s) of the block. Inone example, a bi-orthogonal lapped transform is employed (e.g., lossymode). In a second example, a lossless Hadamard transform (e.g.,lossless Hadamard structure 1020) is utilized (e.g., lossless mode).Next, at 1230, coefficients are quantized. At 1240, the coefficients arescanned. At 1250, the quantized coefficients are encoded.

Referring to FIG. 13, a methodology 1300 for picturedecompression/decoding in accordance with an aspect of the presentinvention is illustrated. At 1310, coefficients are decoded. At 1320,for each macroblock, an inverse transform is performed on low frequencycoefficient(s) for each block. In one example, an inverse bi-orthogonallapped transform is utilized (e.g., lossy mode). In another example, aninverse lossless Hadamard transform is employed (e.g., lossless mode).At 1330, an inverse transform is performed on coefficients for eachblock. In one example, an inverse bi-orthogonal lapped transform isutilized (e.g., lossy mode). In a second example, an inverse losslessHadamard transform is employed (e.g., lossless mode).

Next, referring to FIG. 14, a methodology 1400 for scanning a chunk ofcoefficients in accordance with an aspect of the present invention isillustrated. At 1410, one second level coefficient (e.g., DC component)for each macroblock in the chunk is scanned. Next, at 1420, for eachmacroblock in the chunk, the remaining second level coefficients for themacroblock are scanned. At 1430, group 2 first level coefficients (e.g.,AC components) for each block in the macroblock are scanned. At 1440,group 3 first level coefficients for each block in the macroblock arescanned. At 1450, group 4 first level coefficients for each block in themacroblock are scanned. At 1460, group 5 first level coefficients foreach block in the macroblock are scanned. If there are any moremacroblocks in the chunk which have not been scanned, scanning continuesat 1420. In the exemplary scanning methodology just described, sixgroups of transform coefficients are generated (groups 0 to 5). While itis believed that such a scanning and grouping arrangement produces goodcompression results, any other suitable scanning and grouping patterncan be employed, for example, if compression performance can besacrificed for faster processing. Any such scanning/grouping pattern foremployment in connection with the present invention is intended to fallwithin the scope of the appended claims.

Turning to FIG. 15, a forward mapper component 1510 (e.g., for use bythe color mapper 110) is illustrated. The forward mapper component 1510provides for the original RGB input components to be mapped to the spaceYC_(o)C_(g) (e.g., through scaled versions of Equation (1)). The scalingis such that divisions by 2 are required (as indicated by arrows labeledwith ½), and those can be implemented by right shifts, as previouslydescribed. At first it might appear that the errors introduced by suchshifts would be irrecoverable. However, in a reverse mapper component1520, the outputs of the forward mapper component 1510 are applied inreverse order, such that truncations due to shifts (e.g., the same as inthe forward mapping component 1510) happen, but their effects are nowsubtracted (as indicated by arrows labeled with −½), thus allowing forrecovery of the original data. Thus, the reverse mapper component 1520can recover the original RGB input components (e.g., exactly) from theYC_(o)C_(g) components.

Referring next to FIG. 16, a methodology 1600 for color space mapping isillustrated. For example, the methodology 1600 can be employed by aforward mapper component 1510.

At 1610, an RGB input is received (comprising an R component, a Gcomponent and a B component). At 1620, a Y channel output comprising arepresentation of average light intensity (luminance) of the RGB inputis provided. The Y channel can be provided based on transform (1) above(e.g., Y being based, at least in part, upon R+2G+B). In one example,the Y channel can be provided by using additions and/or shifts ofinformation associated with the RGB input—without multiplications.

At 1630, a C_(o) channel output comprising a representation of colorinformation (chrominance) of the RGB input across a near orangedirection is provided. The C_(o) channel can be provided based ontransform (1) above (e.g., C_(o) being based, at least in part, upon2R−2B). In one example, the C_(o) channel can be provided by usingadditions and/or shifts of information associated with the RGBinput—without multiplications.

At 1640, a C_(g) channel output comprising a representation of colorinformation (chrominance) of the RGB input across a near green directionis provided. The C_(g) channel can be provided based on transform (1)above (e.g., Cg being based, at least in part, upon −R+2G−B). In oneexample, the C_(g) channel can be provided by using additions and/orshifts of information associated with the RGB input—withoutmultiplications.

In another example, the R component, the G component and/or the Bcomponent is able to be recovered by reverse mapping of the YC_(o)C_(g)channels provided according to the methodology 1600.

Turning next to FIG. 17, a methodology 1700 for reverse color spacemapping is illustrated. For example, the methodology 1700 can beemployed by a reverse mapper component 1520.

At 1710, a YC_(o)C_(g) input comprising a Y channel representing anaverage light intensity, a C_(o) channel representing color informationacross a near orange direction, and a C_(g) channel representing colorinformation across a near green direction is received. At 1720, an Rcomponent based, at least in part, upon the YC_(o)C_(g) input isprovided. The R component can be provided based on transform (1) above(e.g., R being based, at least in part, upon Y+C_(o)−C_(g)). In oneexample, the R component can be provided by using additions and/orshifts of information associated with the YC_(o)C_(g) input—withoutmultiplications.

At 1730, a G component based, at least in part, upon the YC_(o)C_(g)input is provided. The R component can be provided based on transform(1) above (e.g., G being based, at least in part, upon Y+C_(g)). In oneexample, the G component can be provided by using additions and/orshifts of information associated with the YC_(o)C_(g) input—withoutmultiplications.

At 1740, a B component based at least in part, upon the YC_(o)C_(g)input is provided. The B component can be provided based on transform(1) above (e.g., B being based, at least in part, upon Y+C_(o)−C_(g)).In one example, the B component can be provided by using additionsand/or shifts of information associated with the YC_(o)C_(g)input—without multiplications.

It is to be appreciated that the system and/or method of the presentinvention can be utilized in an overall compression system facilitatingcompression of text, handwriting, drawings, pictures and the like.Further, those skilled in the art will recognize that the system and/ormethod of the present invention can be employed in a vast array ofdocument image applications, including, but not limited to,photocopiers, document scanners, optical character recognition systems,PDAs, fax machines, digital cameras, digital video cameras and/or videogames.

In order to provide additional context for various aspects of thepresent invention, FIG. 18 and the following discussion are intended toprovide a brief, general description of a suitable operating environment1810 in which various aspects of the present invention may beimplemented. FIG. 19 provides an additional and/or alternative operatingenvironment in which the present invention can operate. While theinvention is described in the general context of computer-executableinstructions, such as program modules, executed by one or more computersor other devices, those skilled in the art will recognize that theinvention can also be implemented in combination with other programmodules and/or as a combination of hardware and software. Generally,however, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular data types. The operating environment 1810 is onlyone example of a suitable operating environment and is not intended tosuggest any limitation as to the scope of use or functionality of theinvention. Other well known computer systems, environments, and/orconfigurations that may be suitable for use with the invention includebut are not limited to, personal computers, hand-held or laptop devices,multiprocessor systems, microprocessor-based systems, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include the above systems ordevices, and the like.

With reference to FIG. 18, an exemplary environment 1810 forimplementing various aspects of the invention includes a computer 1812.The computer 1812 includes a processing unit 1814, a system memory 1816,and a system bus 1818. The system bus 1818 couples system componentsincluding, but not limited to, the system memory 1816 to the processingunit 1814. The processing unit 1814 can be any of various availableprocessors. Dual microprocessors and other multiprocessor architecturesalso can be employed as the processing unit 1814.

The system bus 1818 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 18-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 1816 includes volatile memory 1820 and nonvolatilememory 1822. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1812, such as during start-up, is stored in nonvolatile memory 1822. Byway of illustration, and not limitation, nonvolatile memory 1822 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), or flashmemory. Volatile memory 1820 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 1812 also includes removable/nonremovable, volatile/nonvolatilecomputer storage media. FIG. 18 illustrates, for example a disk storage1824. Disk storage 1824 includes, but is not limited to, devices like amagnetic disk drive, floppy disk drive, tape drive, Jazz drive, Zipdrive, LS-100 drive, flash memory card, or memory stick. In addition,disk storage 1824 can include storage media separately or in combinationwith other storage media including, but not limited to, an optical diskdrive such as a compact disk ROM device (CD-ROM), CD recordable drive(CD-R Drive), CD rewritable drive (CD-RW Drive) or a digital versatiledisk ROM drive (DVD-ROM). To facilitate connection of the disk storagedevices 1824 to the system bus 1818, a removable or non-removableinterface is typically used such as interface 1826.

It is to be appreciated that FIG. 18 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 1810. Such software includes an operatingsystem 1828. Operating system 1828, which can be stored on disk storage1824, acts to control and allocate resources of the computer system1812. System applications 1830 take advantage of the management ofresources by operating system 1828 through program modules 1832 andprogram data 1834 stored either in system memory 1816 or on disk storage1824. It is to be appreciated that the present invention can beimplemented with various operating systems or combinations of operatingsystems.

A user enters commands or information into the computer 1812 throughinput device(s) 1836. Input devices 1836 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the possessing unit 1816through the system bus 1818 via interface port(s) 1838. Interfaceport(s) 1838 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1840 usesome of the same type of ports as input device(s) 1836. Thus, forexample, a USB port may be used to provide input to computer 1812, andto output information from computer 1812 to an output device 1840.Output adapter 1842 is provided to illustrate that there are some outputdevices 1840 like monitors, speakers, and printers among other outputdevices 1840 that require special adapters. The output adapters 1842include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1840and the system bus 1818. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1844.

Computer 1812 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1844. The remote computer(s) 1844 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1812. For purposes of brevity, only a memory storage device 1846 isillustrated with remote computer(s) 1844. Remote computer(s) 1844 islogically connected to computer 1812 through a network interface 1848and then physically connected via communication connection 1850. Networkinterface 1848 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 1502.3, Token Ring/IEEE 1502.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 1850 refers to the hardware/softwareemployed to connect the network interface 1848 to the bus 1818. Whilecommunication connection 1850 is shown for illustrative clarity insidecomputer 1812, it can also be external to computer 1812. Thehardware/software necessary for connection to the network interface 1848includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 19 is a schematic block diagram of a sample computing environment1900 with which the present invention can interact. The system 1900includes one or more client(s) 1910. The client(s) 1910 can be hardwareand/or software (e.g., threads, processes, computing devices). Thesystem 1900 also includes one or more server(s) 1930. The server(s) 1930can also be hardware and/or software (e.g., threads, processes,computing devices). The server(s) 1930 can house threads to performtransformations by employing the present invention, for example. Onepossible communication between a client 1910 and a server 1930 may be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The system 1900 includes a communication framework1950 that can be employed to facilitate communications between theclient(s) 1910 and the server(s) 1930. The client(s) 1910 are operablyconnected to one or more client data store(s) 1960 that can be employedto store information local to the client(s) 1910. Similarly, theserver(s) 1930 are operably connected to one or more server datastore(s) 1940 that can be employed to store information local to theserver(s) 1930.

What has been described above includes examples of the presentinvention. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe present invention, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the presentinvention are possible. Accordingly, the present invention is intendedto embrace all such alterations, modifications and variations that fallwithin the spirit and scope of the appended claims. Furthermore, to theextent that the term “includes” is used in either the detaileddescription or the claims, such term is intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

1. A system for picture data compression, comprising: a transformcomponent that receives input values, the transform component utilizesone of hierarchical lapped bi-orthogonal transforms and hierarchicalHadamard transforms and generates output values; and an adaptive,run-length encoder component that utilizes a backward adaptation rule toencode the output values.
 2. The system of claim 1, a run-lengthparameter of the encoder component is adapted as a function of theencoded output values.
 3. The system of claim 1, further comprising acolor space mapper that maps an input image to a color spacerepresentation and generates the input values.
 4. The system of claim 3,the color space representation is a YC_(o)C_(g) representation of theinput image.
 5. The system of claim 3, the color space representation isa YUV representation of the input image.
 6. The system of claim 1,further comprising a quantizer component that quantizes the outputvalues.
 7. The system of claim 1, further comprising a scanner componentthat scans the output values and provides one-dimensional output values.8. The system of claim 7, the scanner component utilizes a Peano-likescanning order.
 9. A method for picture decompression, comprising:decoding an input bit stream utilizing adaptive, run-length decodingwith an inverse backward adaptation rule to generate decodedcoefficients; generating second level coefficients based at least inpart upon an inverse transform of decoded coefficients; and generatingfirst level coefficients based at least in part upon an inversetransform of the second level coefficients and the decoded coefficients.10. The method of claim 9, the inverse transform is an inversebi-orthogonal lapped transform.
 11. The method of claim 9, the inversetransform is an inverse hierarchical Hadamard transform.
 12. The methodof claim 9, further comprising reverse scanning the decodedcoefficients.
 13. The method of claim 12, reverse scanning furthercomprises utilizing a reverse Peano-like scanning pattern.
 14. Themethod of claim 9, further comprising inverse quantizing the decodedcoefficients.
 15. The method of claim 9, further comprising mapping thefirst level coefficients to an RGB image.
 16. The method of claim 9,mapping the first level coefficients to the RGB image further comprisesmapping from a YUV representation to the RGB image.
 17. The method ofclaim 9, mapping the first level coefficients to the RGB image furthercomprises mapping from a YC_(o)C_(g) representation to the RGB image.18. A system for picture data compression, comprising: means for mappingan input image to a color space representation and generating inputvalues; means for generating output values that comprise transformedcoefficients utilizing at least one of hierarchical Hadamard transformsand hierarchical lapped bi-orthogonal transforms based at least in partupon the input values; and means for adaptive, run-length encoding ofthe output values utilizing a backward adaptation rule.
 19. The systemof claim 18, the color space representation is at least one of aYC_(o)C_(g) representation and a YUV representation.
 20. The system ofclaim 18, further comprising: means for quantizing the output values;and means for scanning the quantized output values to provide onedimensional output values.